matchbox
vector
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matchbox | vector | |
---|---|---|
5 | 96 | |
1,209 | 16,512 | |
1.6% | 5.7% | |
8.8 | 9.9 | |
6 days ago | 3 days ago | |
Go | Rust | |
Apache License 2.0 | Mozilla Public License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
matchbox
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[HELP] PXE Boot without data loss
I also just came across Matchbox.
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Wanna restructure my server and open for suggestions. What's your tech stack?
Software: * matchbox runs on one RPi for provisioning hardware * The remaining RPis and NUCs all run CoreOS * The matchbox server is responsible for deploying CoreOS to everything * Terraform deploys Kubernetes using Typhoon, 3 x masters (RPis) and 3 x workers (NUCs)
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The evolution of my homelab over 1.5 years: from a simple Docker Compose file to a PXE-booted, GitOps-managed multi-node Kubernetes cluster
I have no idea why, but it's this, yes? https://matchbox.psdn.io/
- Help picking cobbler replacement
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We Decided for and Against Ubuntu Core
There have been various different setups here. Have you looked at Matchbox?
> matchbox is a service that matches bare-metal machines to profiles that PXE boot and provision clusters. Machines are matched by labels like MAC or UUID during PXE and profiles specify a kernel/initrd, iPXE config, and Ignition config.
https://github.com/poseidon/matchbox
vector
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Docker Log Observability: Analyzing Container Logs in HashiCorp Nomad with Vector, Loki, and Grafana
job "vector" { datacenters = ["dc1"] # system job, runs on all nodes type = "system" group "vector" { count = 1 network { port "api" { to = 8686 } } ephemeral_disk { size = 500 sticky = true } task "vector" { driver = "docker" config { image = "timberio/vector:0.30.0-debian" ports = ["api"] volumes = ["/var/run/docker.sock:/var/run/docker.sock"] } env { VECTOR_CONFIG = "local/vector.toml" VECTOR_REQUIRE_HEALTHY = "false" } resources { cpu = 100 # 100 MHz memory = 100 # 100MB } # template with Vector's configuration template { destination = "local/vector.toml" change_mode = "signal" change_signal = "SIGHUP" # overriding the delimiters to [[ ]] to avoid conflicts with Vector's native templating, which also uses {{ }} left_delimiter = "[[" right_delimiter = "]]" data=<
- FLaNK AI Weekly 18 March 2024
- Vector: A high-performance observability data pipeline
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Hacks to reduce cloud spend
we are doing something similar with OTEL but we are looking at using https://vector.dev/
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About reading logs
We don't pull logs, we forward logs to a centralized logging service.
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Self hosted log paraer
opensearch - amazon fork of Elasticsearch https://opensearch.org/docs/latestif you do this an have distributed log sources you'd use logstash for, bin off logstash and use vector (https://vector.dev/) its better out of the box for SaaS stuff.
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creating a centralize syslog server with elastic search
I have done something similar in the past: you can send the logs through a centralized syslog servers (I suggest syslog-ng) and from there ingest into ELK. For parsing I am advice to use something like Vector, is a lot more faster than logstash. When you have your logs ingested correctly, you can create your own dashboard in Kibana. If this fit your requirements, no need to install nginx (unless you want to use as reverse proxy for Kibana), php and mysql.
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Show HN: Homelab Monitoring Setup with Grafana
I think there's nothing currently that combines both logging and metrics into one easy package and visualizes it, but it's also something I would love to have.
Vector[1] would work as the agent, being able to collect both logs and metrics. But the issue would then be storing it. I'm assuming the Elastic Stack might now be able to do both, but it's just to heavy to deal with in a small setup.
A couple of months ago I took a brief look at that when setting up logging for my own homelab (https://pv.wtf/posts/logging-and-the-homelab). Mostly looking at the memory usage to fit it on my synology. Quickwit[2] and Log-Store[3] both come with built in web interfaces that reduce the need for grafana, but neither of them do metrics.
- [1] https://vector.dev
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Retaining Logs generated by service running in pod.
Log to stdout/stderr and collect your logs with a tool like vector (vector.dev) and send it to something like Grafana Loki.
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Lightweight logging on RPi?
I would recommend that you run vector as a systems service so you don't have to worry about managing it. Here is a basic config to do that - https://github.com/vectordotdev/vector/blob/master/distribution/systemd/vector.service .
What are some alternatives?
Cobbler - Cobbler is a versatile Linux deployment server
graylog - Free and open log management
netboot - Packages and utilities for network booting
Fluentd - Fluentd: Unified Logging Layer (project under CNCF)
nonguix - Nonguix mirror – pull requests ignored, please use upstream for that
agent - Vendor-neutral programmable observability pipelines.
booty - A simple (i)PXE Server for booting Flatcar-Linux and CoreOS
syslog-ng - syslog-ng is an enhanced log daemon, supporting a wide range of input and output methods: syslog, unstructured text, queueing, SQL & NoSQL.
go-appimage - Go implementation of AppImage tools
OpenSearch - 🔎 Open source distributed and RESTful search engine.
Gource - software version control visualization
tracing - Application level tracing for Rust.